Dynamic cognitive optimization of web applications

ABSTRACT

A method for dynamically and cognitively generating and delivering web build layers for web applications is provided. The method may include receiving, by a computer, file requests associated with web applications. The method may further include, in response to receiving the file requests, identifying resource files associated with the file requests and the web applications by querying, by a computer, at least one application server for the resource files. Additionally, the method may include determining, by a computer, related resource files based on the identified resource files by tracking information and user activity associated with the identified resource files. The method may also include generating web build layers by grouping, by a computer, the determined related resource files. The method may further include delivering the generated optimized web build layers to the web applications based on the tracked information and user activity.

BACKGROUND

The present invention relates generally to the field of computing, andmore specifically, to web applications.

Generally, optimizing a mobile web or desktop web application for use inproduction may increase performance and functionality. The optimizationprocess for web applications is typically a labor-intensive process forapplication developers. For example, during the optimization phase of aweb application's life cycle, a set of optimized web build layers may beused by application developers to optimize the web application.Specifically, each set of optimized web build layers may contain groupsof hypertext markup language (HTML) files that may include cascadingfile sheet (CSS) files, image files, and JavaScript files for one ormore features of the web applications. By using optimized web buildlayers, application developers can limit the amount of small individualfile downloads in response to requests received by web applicationsduring optimization, and in turn, can decrease the network trafficassociated with each request.

SUMMARY

A method for dynamically and cognitively generating and delivering webbuild layers for a web application is provided. The method may includereceiving, by a computer, a file request associated with the webapplication. The method may further include, in response to receivingthe file request, identifying a plurality of resource files associatedwith the received file request and the web application by querying, by acomputer, at least one application server for the plurality of resourcefiles. Additionally, the method may include determining, by a computer,a plurality of related resource files based on the plurality ofidentified resource files by tracking a plurality of information anduser activity associated with the plurality of identified resourcefiles. The method may also include generating a plurality of web buildlayers by grouping, by a computer, the determined related resourcefiles. The method may further include delivering the generated pluralityof web build layers to the web application based on the trackedplurality of information and user activity.

A computer system for dynamically and cognitively generating anddelivering web build layers for a web application is provided. Thecomputer system may include one or more processors, one or morecomputer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving, by a computer, a file request associated with the webapplication. The method may further include, in response to receivingthe file request, identifying a plurality of resource files associatedwith the received file request and the web application by querying, by acomputer, at least one application server for the plurality of resourcefiles. Additionally, the method may include determining, by a computer,a plurality of related resource files based on the plurality ofidentified resource files by tracking a plurality of information anduser activity associated with the plurality of identified resourcefiles. The method may also include generating a plurality of web buildlayers by grouping, by a computer, the determined related resourcefiles. The method may further include delivering the generated pluralityof web build layers to the web application based on the trackedplurality of information and user activity.

A computer program product for dynamically and cognitively generatingand delivering web build layers for a web application is provided. Thecomputer program product may include one or more computer-readablestorage devices and program instructions stored on at least one of theone or more tangible storage devices, the program instructionsexecutable by a processor. The computer program product may includeprogram instructions to receive, by a computer, a file requestassociated with the web application. The computer program product mayfurther include program instructions to, in response to receiving thefile request, identify a plurality of resource files associated with thereceived file request and the web application by querying, by acomputer, at least one application server for the plurality of resourcefiles. Additionally, the computer program product may also includeprogram instructions to determine, by a computer, a plurality of relatedresource files based on the plurality of identified resource files bytracking a plurality of information and user activity associated withthe plurality of identified resource files. The computer program productmay further include program instructions to generate a plurality of webbuild layers by grouping, by a computer, the determined related resourcefiles. The computer program product may also include programinstructions to deliver the generated plurality of web build layers tothe web application based on the tracked plurality of information anduser activity.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to oneembodiment;

FIG. 2A is an example of resource files associated with an HTML webpageaccording to one embodiment;

FIG. 2B is an example of optimized web build layers according to oneembodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program for dynamically and cognitively generating and deliveringoptimized web build layers for web applications according to oneembodiment;

FIG. 4 is a block diagram of the system architecture of a program fordynamically and cognitively generating and delivering optimized webbuild layers for web applications according to one embodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. In the description, details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the presented embodiments.

Embodiments of the present invention relate generally to the field ofcomputing, and more particularly, to web applications. The followingdescribed exemplary embodiments provide a system, method and programproduct for dynamically and cognitively generating and deliveringoptimized web build layers for web applications. Specifically, thepresent embodiment has the capacity to improve the technical fieldassociated with optimizing web applications by dynamically andcognitively generating optimized web build layers based on cognitivelyidentified information and user activity associated with the webapplications. More specifically, in response to receiving file requestsfor web content associated with web applications, the present embodimentmay identify resource files associated with the file request, and maydynamically and cognitively generate optimized web build layers usingthe identified resource files based on tracked information and useractivity.

As previously described with respect to web applications, a set ofoptimized web build layers may be used to optimize the web applications.For example, each set of optimized web build layers may contain groupsof hypertext markup language (HTML) files that may include cascadingfile sheet (CSS) files, image files, and JavaScript files for one ormore features of the application. Furthermore, by using optimized webbuild layers, application developers can limit the amount of smallindividual file downloads performed in response to file downloadrequests and thereby decrease network traffic. However, and aspreviously described, the optimization process for web applications is alabor-intensive process for application developers. Specifically,application developers typically have to determine how and what files togroup together in the optimized build layer, which can result in errorssuch as misgroupings in the optimized web build layers, or failing toadd files to the optimized build layer, resulting in greater networktraffic to transfer individual optimized files. Additionally, theoptimized web build layers are generally delivered to all end users ofweb applications without taking into account the type of end userreceiving the optimized web application and the features used by the endusers, such as whether the end user is an administrator or user, orwhether the end user is a business user or personal user of the webapplication. As such, it may be advantageous, among other things, toprovide a system, method and program product for dynamically andcognitively generating and delivering optimized web build layers for webapplications. Specifically, in response to receiving file requestsassociated with web applications, the system, method, and programproduct may identify resource files associated with the file requests,and may dynamically and cognitively generate optimized web build layersusing the identified resource files based on tracked information anduser activity.

According to at least one implementation of the present embodiment, filerequests associated with web applications may be received. Next,resource files associated with the received file requests may beidentified. Then, based on the identified resource files, relatedresource files may be determined. Next, optimized web build layers maybe generated by grouping the related resource files. Then, the optimizedweb build layers may be delivered to the web applications based ontracked information and user activity.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, method,and program product for dynamically and cognitively optimizing webapplications.

According to at least one implementation, file requests associated withweb applications may be received. Next, resource files associated withthe received file requests may be identified. Then, based on theidentified resource files, related resource files may be determined.Next, optimized web build layers may be generated by grouping therelated resource files. Then, the optimized web build layers may bedelivered to the web applications based on tracked information and useractivity.

Referring now to FIG. 1, an exemplary networked computer environment 100in accordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a cognitive webapplication optimization program 108A and a software program 114. Thesoftware program 114 may be an application program such as a web ormobile web program/app, an email program/app, or a mobile program/appwith network access. The cognitive web application optimization program108A may communicate with the software program 114. The networkedcomputer environment 100 may also include a server 112 that is enabledto run a cognitive web application optimization program 108B and acommunication network 110. The networked computer environment 100 mayinclude a plurality of computers 102 and servers 112, only one of whichis shown for illustrative brevity.

According to at least one implementation, the present embodiment mayalso include a database 116, which may be running on server 112. Thecommunication network 110 may include various types of communicationnetworks, such as a wide area network (WAN), local area network (LAN), atelecommunication network, a wireless network, a public switched networkand/or a satellite network. It may be appreciated that FIG. 1 providesonly an illustration of one implementation and does not imply anylimitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

The client computer 102 may communicate with server computer 112 via thecommunications network 110. The communications network 110 may includeconnections, such as wire, wireless communication links, or fiber opticcables. As will be discussed with reference to FIG. 4, server computer112 may include internal components 800 a and external components 900 a,respectively, and client computer 102 may include internal components800 b and external components 900 b, respectively. Server computer 112may also operate in a cloud computing service model, such as Software asa Service (SaaS), Platform as a Service (PaaS), or Infrastructure as aService (IaaS). Server 112 may also be located in a cloud computingdeployment model, such as a private cloud, community cloud, publiccloud, or hybrid cloud. Client computer 102 may be, for example, amobile device, a set top box, a television device, a telephone, apersonal digital assistant, a netbook, a laptop computer, a tabletcomputer, a desktop computer, or any type of computing device anddynamic digital advertisement display capable of running a program andaccessing a network. According to various implementations of the presentembodiment, the cognitive web application optimization program 108A,108B may interact with a database 116 that may be embedded in variousstorage devices, such as, but not limited to a mobile device 102, anetworked server 112, or a cloud storage service.

According to the present embodiment, a program, such as a cognitive webapplication optimization program 108A and 108B may run on the clientcomputer 102 or on the server computer 112 via a communications network110. The cognitive web application optimization program 108A, 108B maydynamically and cognitively optimize web applications. Specifically, auser using a computer, such as computer 102, may run a cognitive webapplication optimization program 108A, 108B, that interacts with asoftware program 114, such as a web application to receive file requestsbased on content associated with the web application, and in response toreceiving the file requests, the cognitive web application optimizationprogram 108A, 108B may identify resource files associated with the filerequests, and may dynamically and cognitively generate optimized webbuild layers using the identified resource files based on trackedinformation and user activity.

Referring now to FIG. 2A, an example of resource files 200 associatedwith an HTML webpage according to one embodiment is depicted.Specifically, for example, web applications may include internetwebpages that may request files to load web content associated with theweb applications. Furthermore, an internet webpage may be an HTMLwebpage that includes an HTML resource file 202, which may furtherinclude individual resource files 200 that correspond to differentfeatures, texts, and uniform resource locator (URL) links associatedwith the HTML webpage as well as with the web application. Morespecifically, the HTML resource file 202 may include resource files 200such as cascading file sheet (CSS) files 204, image files 208, andJavaScript files 206. Without optimization, each resource file 200 istypically individually loaded as the HTML webpage is loaded.

Referring now to FIG. 2B, an example of optimized web build layers 210according to one embodiment of the present invention is depicted. Aspreviously described in FIG. 2A, an internet webpage may be an HTMLwebpage that includes an HTML resource file 202 (FIG. 2A), which mayfurther include individual resource files 200 (FIG. 2A) such as CSSfiles 204 (FIG. 2A), image files 208 (FIG. 2A), and JavaScript files 206(FIG. 2A). Also, as previously described, without optimization, eachresource file 200 (FIG. 2A) is typically individually loaded as the HTMLwebpage is loaded. Specifically, when the HTML webpage is loaded withoutoptimization, the HTML webpage may make numerous requests to acorresponding web application server for the different resource files200 (FIG. 2A) associated with the HTML webpage. As such, the number ofresource file download requests and the network traffic associated withweb applications may be overwhelming. Therefore, the cognitive webapplication optimization program 108A, 108B (FIG. 1) may dynamicallygenerate optimized web build layers, such as optimized web build layers214 a, 214 b, and 214 c, which may include resource files that may bedelivered in a package to the web applications instead individually,thereby reducing the number of resource file downloads to satisfy thereceived file requests and decreasing the network traffic associatedwith web applications. More specifically, the cognitive web applicationoptimization program 108A, 108B (FIG. 1) may generate the optimized webbuild layers by dynamically and cognitively identifying and grouping theresource files 200 (FIG. 2A) based on tracked information and useractivity that is associated with the resource files 200 (FIG. 2A). Forexample, the cognitive web application optimization program 108A, 108B(FIG. 1) may generate optimized web build layers 210 by grouping theresource files 200 (FIG. 2A) that are associated with a toolbar of a webapplication based on the similarity in metadata and/or code. Also, forexample, the cognitive web application optimization program 108A, 108B(FIG. 1) may generate the optimized web build layers by dynamically andcognitively identifying like or related resource files 200 (FIG. 2A) ofthe same type and grouping the related resource files 200 (FIG. 2A),such as by grouping the related CSS files 204 (FIG. 2A) to generateoptimized build layer 214 a, grouping the related image files 208 (FIG.2A) to generate optimized build layer 214 b, and grouping the relatedJavaScript files 206 (FIG. 2A) to generate optimized build layer 214 c.

Referring now to FIG. 3, an operational flowchart 300 illustrating thesteps carried out by a program for dynamically and cognitivelygenerating and delivering optimized web build layers for webapplications according to one embodiment is depicted. At 302, thecognitive web application optimization program 108A, 108B (FIG. 1) mayreceive file requests associated with web applications. Specifically,for example, the cognitive web application optimization program 108A,108B (FIG. 1) may receive, via a computer, file requests associated withweb applications for loading web content on the web application. Forexample, the file requests may be correspond to different features,texts, images, and URL links associated with HTML webpages on the webapplications.

Then, at 304, the cognitive web application optimization program 108A,108B (FIG. 1) may identify the resource files 200 (FIG. 2A) associatedwith the received file requests and the web applications. Specifically,for example, the cognitive web application optimization program 108A,108B (FIG. 1) may receive file requests associated with an HTML webpageof a web application. Thereafter, according to one embodiment, thecognitive web application optimization program 108A, 108B (FIG. 1) mayanalyze the HTML webpage and web application associated with thereceived requests and, via a computer, query an application server toidentify the resource files 200 (FIG. 2A) associated with the HTMLwebpage and web application. More specifically, and as previouslydescribed in FIG. 2A, the HTML webpage may include an HTML resource file202 (FIG. 2A), which may further include individual resource files 200(FIG. 2A) such as CSS files 204 (FIG. 2A), image files 208 (FIG. 2A),and JavaScript files 206 (FIG. 2A). Therefore, in response to receivingthe file requests, the cognitive web application optimization program108A, 108B (FIG. 1) may analyze the HTML webpage to identify the CSSfiles 204 (FIG. 2A), the image files 208 (FIG. 2A), and the JavaScriptfiles 206 (FIG. 2A) associated with that HTML webpage.

Next, at 306, the cognitive web application optimization program 108A,108B (FIG. 1) may determine related resource files 200 (FIG. 2A) bydynamically and cognitively tracking information and user activityassociated with the identified resource files 200 (FIG. 2A).Specifically, according to one embodiment, the cognitive web applicationoptimization program 108A, 108B (FIG. 1) may dynamically analyze theidentified resource files 200 (FIG. 2A) to, via a computer, dynamicallyand cognitively track the information and user activity associated withthe identified resource files 200 (FIG. 2A), and may use an applicationserver, such as server 114 (FIG. 1), to gather and store the dynamicallyand cognitively tracked information and user activity based on theanalysis. More specifically, the cognitive web application optimizationprogram 108A, 108B (FIG. 1) may dynamically and cognitively trackinformation and user activity such as: dependency relationships betweenthe identified resource files 200 (FIG. 2A), whereby tracking thedependency relationship may include identifying whether the identifiedresource files 200 (FIG. 2A) are interdependent and/or are used together(i.e., identified resource file A may import identified resource fileB); information based on the identified resource files 200 (FIG. 2A)that are specifically associated with the received requests; metadataassociated with the identified resource files 200 (FIG. 2A); HTTPheaders used in the received requests; the type and size of theidentified resource files 200 (FIG. 2A); the frequency with which a usermay or may not use the identified resources files 200 (FIG. 2A) and/orthe features associated with the identified resource files 200 (FIG.2A); and the type of user or target audience for the identified resourcefiles 200 (FIG. 2A), such as whether the identified resource files 200(FIG. 2A) are restricted to administrators, employees, or otherspecifically identified users, and/or whether the identified resourcefiles 200 (FIG. 2A) are used for business or personal use. Thereafter,the cognitive web application optimization program 108A, 108B (FIG. 1)may determine that identified resource files 200 (FIG. 2A) may berelated by determining similarities between the identified resourcefiles 200 (FIG. 2A) based on one or more of the dynamically andcognitively tracked information and user activity.

For example, and as previously described in FIG. 2B, based on trackedinformation and user activity, the cognitive web applicationoptimization program 108A, 108B (FIG. 1) may query the applicationserver and determine that identified resource files 200 (FIG. 2A) arerelated based on the identified resource files 200 (FIG. 2A) beinglocated on a toolbar of a web application and/or based on a similarityin metadata and/or code associated with the identified resource files200 (FIG. 2A) for the toolbar. Also, for example, based on the trackedinformation and user activity, the cognitive web applicationoptimization program 108A, 108B (FIG. 1) may determine that identifiedresource files 200 (FIG. 2A) of the same type are related resource files200 (FIG. 2A), such as by determining that the identified CSS files 204(FIG. 2A) may be related resource files 200 (FIG. 2A), determining thatthe related image files 208 (FIG. 2A) may be related resource files 200(FIG. 2A), and determining that the related JavaScript files 206 (FIG.2A) may be related resource files 200 (FIG. 2A).

Then, at 308, the cognitive web application optimization program 108A,108B (FIG. 1) may generate optimized web build layers 210 (FIG. 2B) bycognitively grouping the determined related resource files 200 (FIG.2A). As previously described at step 306, the cognitive web applicationoptimization program 108A, 108B (FIG. 1) may determine the relatedresource files 200 (FIG. 2A) based on dynamically and cognitivelytracking information and user activity associated with the identifiedresource files 200 (FIG. 2A). Thereafter, the cognitive web applicationoptimization program 108A, 108B (FIG. 1) may generate optimized webbuild layers 210 (FIG. 2B) by determining the optimal (or best)groupings for the determined related resource files 200 (FIG. 2A).Specifically, and as previously described at step 306, the cognitive webapplication optimization program 108A, 108B (FIG. 1) may dynamicallyanalyze the identified resource files 200 (FIG. 2A) to dynamically andcognitively track information and user activity. Then, for example,based on the tracked information and user activity, the cognitive webapplication optimization program 108A, 108B (FIG. 1) may determine thatidentified resource files 200 (FIG. 2A) of the same type may be relatedresource files 200 (FIG. 2A), such as by determining that the identifiedCSS files 204 (FIG. 2A) may be related resource files 200 (FIG. 2A),determining that the related image files 208 (FIG. 2A) may be relatedresource files 200 (FIG. 2A), and determining that the relatedJavaScript files 206 (FIG. 2A) may be related resource files 200 (FIG.2A). Furthermore, based on the dynamically and cognitively trackedinformation and user activity, such as file size and network traffic,the cognitive web application optimization program 108A, 108B (FIG. 1)may determine that the related resource files 200 (FIG. 2A) of the sametype may be optimally grouped to generate and deliver the optimized webbuild layers 210 (FIG. 2B). Therefore, for example, the cognitive webapplication optimization program 108A, 108B (FIG. 1) may generate theoptimized web build layers 210 (FIG. 2B) by grouping the related CSSfiles 204 (FIG. 2A) to generate optimized build layer 214 a (FIG. 2B),grouping the related image files 208 (FIG. 2A) to generate optimizedbuild layer 214 b (FIG. 2B), and grouping the related JavaScript files206 (FIG. 2A) to generate optimized build layer 214 c (FIG. 2B).

Next, at 310, the cognitive web application optimization program 108A,108B (FIG. 1) may deliver the generated optimized web build layers 210(FIG. 2B) to the web applications. More specifically, according to oneembodiment, the cognitive web application optimization program 108A,108B (FIG. 1) may send the generated optimized web build layers 210(FIG. 2B) to the web applications based on the dynamically andcognitively tracked user activity. As previously described at step 306,the cognitive web application optimization program 108A, 108B (FIG. 1)may dynamically analyze the identified resource files 200 (FIG. 2A) todynamically and cognitively track information and user activity thatmay, for example, include the frequency with which a user may or may notuse the identified resources files 200 (FIG. 2A) and/or the featuresassociated with the identified resource files 200 (FIG. 2A), and thetype of user or target audience receiving the generated optimized webbuild layers 210 (FIG. 2B), such as whether the generated optimized webbuild layers 210 (FIG. 2B) include identified resource files 200 (FIG.2A) that are restricted to administrators, employees, or otherspecifically identified users only, and/or whether the identifiedresource files 200 (FIG. 2A) are used for business or personal use. Forexample, the cognitive web application optimization program 108A, 108B(FIG. 1) may send the optimized web build layers 210 (FIG. 2B) thatinclude the resource files 200 (FIG. 2A) that may be frequently used bya user (such as resource files associated with links frequently clickedon by a user), and may not send the optimized web build layers 210 (FIG.2B) that do not include frequently used identified resource files 200(FIG. 2A). Also, for example, the cognitive web application optimizationprogram 108A, 108B (FIG. 1) may not send to a general end-user theoptimized web build layers 210 (FIG. 2B) that include identifiedresource files 200 (FIG. 2A) restricted to administrators only.

It may be appreciated that FIGS. 2A, 2B, and 3 provide onlyillustrations of one implementation and do not imply any limitationswith regard to how different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements. For example, based on the dynamicallyand cognitively tracked information and user activity, the cognitive webapplication optimization program 108A, 108B (FIG. 1) may further learnand suggest changes to the resource files 200 (FIG. 2A) associated withweb applications to further reduce the amount of file downloadrequests/transactions and network traffic.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 800, 900 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 800, 900 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 800, 900 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 (FIG. 1), and network server 112 (FIG. 1)include respective sets of internal components 800 a, b and externalcomponents 900 a, b illustrated in FIG. 4. Each of the sets of internalcomponents 800 a, b includes one or more processors 820, one or morecomputer-readable RAMs 822, and one or more computer-readable ROMs 824on one or more buses 826, and one or more operating systems 828 and oneor more computer-readable tangible storage devices 830. The one or moreoperating systems 828, the software program 114 (FIG. 1) and thecognitive web application optimization program 108A (FIG. 1) in clientcomputer 102 (FIG. 1), and the cognitive web application optimizationprogram 108B (FIG. 1) in network server computer 112 (FIG. 1) are storedon one or more of the respective computer-readable tangible storagedevices 830 for execution by one or more of the respective processors820 via one or more of the respective RAMs 822 (which typically includecache memory). In the embodiment illustrated in FIG. 4, each of thecomputer-readable tangible storage devices 830 is a magnetic diskstorage device of an internal hard drive. Alternatively, each of thecomputer-readable tangible storage devices 830 is a semiconductorstorage device such as ROM 824, EPROM, flash memory or any othercomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 800 a, b, also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as a cognitiveweb application optimization program 108A and 108B (FIG. 1), can bestored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832, and loaded into the respective hard drive 830.

Each set of internal components 800 a, b also includes network adaptersor interfaces 836 such as a TCP/IP adapter cards, wireless Wi-Fiinterface cards, or 3G or 4G wireless interface cards or other wired orwireless communication links. The cognitive web application optimizationprogram 108A (FIG. 1) and software program 114 (FIG. 1) in clientcomputer 102 (FIG. 1), and the cognitive web application optimizationprogram 108B (FIG. 1) in network server 112 (FIG. 1) can be downloadedto client computer 102 (FIG. 1) from an external computer via a network(for example, the Internet, a local area network or other, wide areanetwork) and respective network adapters or interfaces 836. From thenetwork adapters or interfaces 836, the cognitive web applicationoptimization program 108A (FIG. 1) and software program 114 (FIG. 1) inclient computer 102 (FIG. 1) and the cognitive web applicationoptimization program 108B (FIG. 1) in network server computer 112(FIG. 1) are loaded into the respective hard drive 830. The network maycomprise copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers, and/or edge servers.

Each of the sets of external components 900 a, b can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800 a, b also includes device drivers840 to interface to computer display monitor 920, keyboard 930, andcomputer mouse 934. The device drivers 840, R/W drive or interface 832,and network adapter or interface 836 comprise hardware and software(stored in storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 500A, desktop computer 500B, laptop computer500C, and/or automobile computer system 500N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 500A-Nshown in FIG. 5 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 600provided by cloud computing environment 500 (FIG. 5) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 6 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and cognitive web application optimization96. A cognitive web application optimization program 108A, 108B (FIG. 1)may be offered “as a service in the cloud” (i.e., Software as a Service(SaaS)) for applications running on mobile devices 102 (FIG. 1) and maydynamically and cognitively generate and deliver optimized web buildlayers for web applications.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for dynamically and cognitivelygenerating and delivering web build layers for a web application, themethod comprising: receiving, by a computer, a file request associatedwith the web application, wherein the received file request comprises arequest for loading content on the web application; in response toreceiving the file request, identifying a plurality of resource filesassociated with the received file request and the web application byquerying, by a computer, at least one application server for theplurality of resource files; determining, by a computer, whether theplurality of resource files are related based on tracked information andtracked user activity associated with the plurality of resource files,wherein the determining whether the plurality of resource files arerelated based on the tracked information and the tracked user activityassociated with the plurality of resource files comprises determiningwhether there is a dependency relationship between the plurality ofresource files, determining a frequency with which a user uses one ormore of the plurality of resource files, and determining whether one ormore of the plurality of resource files are restricted to a particulartype of user; generating a plurality of web build layers comprising thedetermined plurality of related resource files, wherein generatingcomprises determining different types of relationships between theplurality of identified resource files based on the tracked plurality ofinformation and user activity, and grouping the plurality of identifiedresource files based on the determined different types of relationships,wherein in response to generating a plurality of different groupings ofthe plurality of determined related resource files based on thedetermined different types of relationships, selecting for delivery tothe web application one or more groupings out of the plurality ofdifferent groupings based on a file size associated with each of the oneor more groupings and network traffic associated with a network, whereinone or more of the plurality of the determined related resource filesare included in two or more groupings associated with the plurality ofdifferent groupings; and based on the selection of the one or moregroupings, delivering the generated plurality of web build layers to theweb application based on the tracked plurality of information and useractivity.